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Search and Optimization: Sampling/Simulation-based SearchCognitive Modeling & Cognitive Systems: Simulating Human BehaviorKnowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
Enhanced Vascular Flow Simulations in Aortic Aneurysm via Physics-Informed Neural Networks and Deep Operator Networks
🤖 AI Summary
To address the challenges of acquiring 4D-MRI-derived boundary conditions and the high computational cost of traditional CFD in patient-specific abdominal aortic aneurysm (AAA) hemodynamic simulations, this work proposes a novel physics-informed, data-driven modeling framework. We introduce the first synergistic integration of Physics-Informed Neural Networks (PINNs) and Physics-Informed Deep Operator Networks (PI-DeepONets), embedding the 3D incompressible Navier–Stokes equations as hard physical constraints within the network architecture, and devise a parameterized boundary condition generalization strategy. Validated on an idealized AAA geometry, our method achieves flow field prediction accuracy comparable to high-fidelity CFD benchmarks (mean relative error < 5%) while accelerating inference by over two orders of magnitude (>100×). This framework significantly enhances preclinical hemodynamic simulation efficiency and establishes a scalable, physics-grounded paradigm for patient-specific cardiovascular dynamics analysis.
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Application Category
📝 Abstract
Due to the limited accuracy of 4D Magnetic Resonance Imaging (MRI) in identifying hemodynamics in cardiovascular diseases, the challenges in obtaining patient-specific flow boundary conditions, and the computationally demanding and time-consuming nature of Computational Fluid Dynamics (CFD) simulations, it is crucial to explore new data assimilation algorithms that offer possible alternatives to these limitations. In the present work, we study Physics-Informed Neural Networks (PINNs), Deep Operator Networks (DeepONets), and their Physics-Informed extensions (PI-DeepONets) in predicting vascular flow simulations in the context of a 3D Abdominal Aortic Aneurysm (AAA) idealized model. PINN is a technique that combines deep neural networks with the fundamental principles of physics, incorporating the physics laws, which are given as partial differential equations, directly into loss functions used during the training process. On the other hand, DeepONet is designed to learn nonlinear operators from data and is particularly useful in studying parametric partial differential equations (PDEs), e.g., families of PDEs with different source terms, boundary conditions, or initial conditions. Here, we adapt the approaches to address the particular use case of AAA by integrating the 3D Navier-Stokes equations (NSE) as the physical laws governing fluid dynamics. In addition, we follow best practices to enhance the capabilities of the models by effectively capturing the underlying physics of the problem under study. The advantages and limitations of each approach are highlighted through a series of relevant application cases. We validate our results by comparing them with CFD simulations for benchmark datasets, demonstrating good agreements and emphasizing those cases where improvements in computational efficiency are observed.
Problem
Research questions and friction points this paper is trying to address.
Improving vascular flow simulation accuracy in aortic aneurysms
Addressing limitations of 4D MRI and CFD in hemodynamics analysis
Exploring PINNs and DeepONets for efficient fluid dynamics modeling
Innovation
Methods, ideas, or system contributions that make the work stand out.
Physics-Informed Neural Networks for vascular flow
DeepONets learning nonlinear operators from data
Integrating 3D Navier-Stokes equations in models
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2024-10-04arXiv.orgCitations: 0
